Data Cleaning Lab
Trace raw rows through diagnosis, imputation, duplicate handling, IQR outlier evidence, scaling, and categorical encoding.
A small table-first data-quality studio. Every transformation is explicit: missing cells are detected, replacement statistics are calculated, exact duplicate records are identified, outliers are flagged or capped with the 1.5×IQR rule, numeric features are z-scored, and city categories become one-hot features.
Step by step
- Inspect the untouched table first, then diagnose missing cells and exact duplicate feature rows without mutating the source.
- Choose mean or median numeric imputation and use the modal category for missing categorical values.
- Remove exact duplicate records without treating the row identifier as a model feature.
- Calculate Q1, Q3, IQR, and the 1.5×IQR fences; flag outliers before deciding whether to cap them.
- Standardize numeric columns with z = (x−μ)/σ.
- One-hot encode the categorical city field so categories do not receive a fake numeric ordering.
Core formulas
IQR fences
[Q1 − 1.5·IQR, Q3 + 1.5·IQR]A common descriptive rule for flagging unusually distant values.
Z-score
z = (x − μ) / σCenter a numerical feature at zero and express distance in standard deviations.
One-hot encoding
category → [0,…,1,…,0]Represent an unordered category with separate binary indicator features.
When to use Data Cleaning Lab
- Learning why data quality decisions should be visible before model training.
- Understanding missing-value, duplicate, outlier, scaling, and categorical encoding mechanics.
- Practicing transformation auditability before moving to feature engineering.